Abstract
We present an efficient computational architecture designed using supervised machine learning model to predict amyloid fibril forming protein segments, named AmylPepPred. The proposed prediction model is based on bio-physio-chemical properties of primary sequences and auto-correlation function of their amino acid indices. AmylPepPred provides a user friendly web interface for the researchers to easily observe the fibril forming and non-fibril forming hexmers in a given protein sequence. We expect that this stratagem will be highly encouraging in discovering fibril forming regions in proteins thereby benefit in finding therapeutic agents that specifically aim these sequences for the inhibition and cure of amyloid illnesses.AvailabilityAmylPepPred is available freely for academic use at www.zoommicro.in/amylpeppred
Highlights
Amyloid fibril forming proteins are found to be related to amyloid illnesses
AmylPepPred provides an open access platform that enables easy and comprehensive retrieval of fibril forming short stretches that compensates the gap in existing amyloid fibril prediction tools by maintaining equilibrium between sensitivity and specificity
The training dataset (Amylpreddataset) has been compiled using experimentally proved proteins related to amyloidosis and proteins with no experimentally determined amyloidogenic regions as described in [6, 7]
Summary
Amyloid fibril forming proteins are found to be related to amyloid illnesses. Recent experiments suggest that it is not the whole protein; rather short fragments are responsible for amyloidosis [1]. AmylPepPred provides an open access platform that enables easy and comprehensive retrieval of fibril forming short stretches that compensates the gap in existing amyloid fibril prediction tools by maintaining equilibrium between sensitivity and specificity. This prediction model is a practical implementation of the computational architecture depicted in figure 1 that purely follows a sequence-based design strategy. The length of wet lab proven positive regions of proteins varies. By selecting appropriate radio buttons, user can view the fibril forming, non-fibril forming hexmer sequences or both along with positions
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